where \(\hat{{H}}\)  represents the reconstructed frame from DMR, and \({H}_g\)  represents the ground truth frame. We use a residual block similar to [1]}, which has a {conv + ReLU} and a {conv} layer at the beginning and end, with 17 intermediate layers of {conv + BN + ReLU}. The kernel size is \(3\times 3\)  with stride of 1. The loss function for our denoiser is the mean squared error of \(\hat{{H}}\)  and \({R}\) . During training, we augment data by randomizing the configuration parameters (including the running epochs) in DMR, summarized in Table REF . The goal of this augmentation is 1) to prevent overfitting; 2) to enforce learning of our DMR process; 3) to alleviate effects due to non-optimal parameter tunning. Our denoiser is single-frame, as we seek to enhance each DMR output frame iteratively without comprimising the variety of DMR fusion settings.
<TABLE>